Overview

Dataset statistics

Number of variables41
Number of observations52000
Missing cells294880
Missing cells (%)13.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory87.4 MiB
Average record size in memory1.7 KiB

Variable types

Text5
DateTime1
Categorical22
Numeric6
Boolean7

Alerts

amount_brl is highly overall correlated with amount_origHigh correlation
amount_orig is highly overall correlated with amount_brlHigh correlation
auth_3ds is highly overall correlated with capture_method and 5 other fieldsHigh correlation
capture_method is highly overall correlated with auth_3ds and 4 other fieldsHigh correlation
card_brand is highly overall correlated with pix and 2 other fieldsHigh correlation
card_present is highly overall correlated with auth_3ds and 5 other fieldsHigh correlation
channel is highly overall correlated with pixHigh correlation
country_risk_geo is highly overall correlated with country_risk_receiver and 3 other fieldsHigh correlation
country_risk_ip is highly overall correlated with country_risk_sender and 3 other fieldsHigh correlation
country_risk_receiver is highly overall correlated with country_risk_geo and 3 other fieldsHigh correlation
country_risk_sender is highly overall correlated with country_risk_ip and 2 other fieldsHigh correlation
cross_border is highly overall correlated with geo_country and 3 other fieldsHigh correlation
currency is highly overall correlated with fx_to_brl and 1 other fieldsHigh correlation
eci is highly overall correlated with auth_3ds and 5 other fieldsHigh correlation
fx_to_brl is highly overall correlated with currency and 1 other fieldsHigh correlation
geo_country is highly overall correlated with country_risk_geo and 5 other fieldsHigh correlation
geolocation_lat is highly overall correlated with cross_border and 2 other fieldsHigh correlation
geolocation_lon is highly overall correlated with country_risk_geo and 4 other fieldsHigh correlation
installments is highly overall correlated with pix_flowHigh correlation
ip_anomaly is highly overall correlated with country_risk_ip and 1 other fieldsHigh correlation
ip_country is highly overall correlated with country_risk_ip and 3 other fieldsHigh correlation
ip_proxy_vpn_tor is highly overall correlated with sanctions_screening_hitHigh correlation
issuing_or_acquiring is highly overall correlated with pix and 4 other fieldsHigh correlation
payment_method is highly overall correlated with pix and 2 other fieldsHigh correlation
pix is highly overall correlated with auth_3ds and 9 other fieldsHigh correlation
pix_flow is highly overall correlated with currency and 7 other fieldsHigh correlation
receiver_country is highly overall correlated with country_risk_geo and 5 other fieldsHigh correlation
receiver_entity_type is highly overall correlated with issuing_or_acquiring and 2 other fieldsHigh correlation
sanctions_screening_hit is highly overall correlated with auth_3ds and 7 other fieldsHigh correlation
sender_country is highly overall correlated with country_risk_ip and 2 other fieldsHigh correlation
sender_entity_type is highly overall correlated with issuing_or_acquiring and 2 other fieldsHigh correlation
transaction_type is highly overall correlated with auth_3ds and 8 other fieldsHigh correlation
sender_entity_type is highly imbalanced (52.9%)Imbalance
currency is highly imbalanced (82.0%)Imbalance
fx_to_brl is highly imbalanced (82.0%)Imbalance
status is highly imbalanced (73.6%)Imbalance
pix_flow is highly imbalanced (52.9%)Imbalance
geo_country is highly imbalanced (67.3%)Imbalance
ip_country is highly imbalanced (91.4%)Imbalance
ip_anomaly is highly imbalanced (96.5%)Imbalance
device_rooted is highly imbalanced (80.5%)Imbalance
sender_country is highly imbalanced (94.5%)Imbalance
receiver_country is highly imbalanced (70.4%)Imbalance
country_risk_geo is highly imbalanced (85.6%)Imbalance
country_risk_ip is highly imbalanced (93.7%)Imbalance
country_risk_sender is highly imbalanced (97.6%)Imbalance
country_risk_receiver is highly imbalanced (87.0%)Imbalance
sanctions_screening_hit is highly imbalanced (99.9%)Imbalance
payment_method has 34070 (65.5%) missing valuesMissing
issuing_or_acquiring has 34070 (65.5%) missing valuesMissing
pix_flow has 20453 (39.3%) missing valuesMissing
card_brand has 34070 (65.5%) missing valuesMissing
card_present has 34070 (65.5%) missing valuesMissing
auth_3ds has 44905 (86.4%) missing valuesMissing
eci has 44905 (86.4%) missing valuesMissing
ip_proxy_vpn_tor has 48337 (93.0%) missing valuesMissing
transaction_id has unique valuesUnique
device_fingerprint has unique valuesUnique
ip_address has unique valuesUnique

Reproduction

Analysis started2026-02-07 14:28:08.204721
Analysis finished2026-02-07 14:28:17.631206
Duration9.43 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

transaction_id
Text

Unique 

Distinct52000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
2026-02-07T11:28:17.830063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters676000
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52000 ?
Unique (%)100.0%

Sample

1st rowT9HIMVHJ8TMK7
2nd rowTRY1GG0393ZEQ
3rd rowTYVX10N3OXT1H
4th rowTEXB9LV0C1BOI
5th rowT9HZ6Z1DWO12V
ValueCountFrequency (%)
t9himvhj8tmk71
 
< 0.1%
try1gg0393zeq1
 
< 0.1%
tyvx10n3oxt1h1
 
< 0.1%
texb9lv0c1boi1
 
< 0.1%
t9hz6z1dwo12v1
 
< 0.1%
t394id0rl2n3l1
 
< 0.1%
tpqmbmp27ngzk1
 
< 0.1%
t1aj938l2psxf1
 
< 0.1%
tx0gk853yyawo1
 
< 0.1%
tuilbkr4582ye1
 
< 0.1%
Other values (51990)51990
> 99.9%
2026-02-07T11:28:18.004113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T69433
 
10.3%
B17579
 
2.6%
R17565
 
2.6%
917565
 
2.6%
I17515
 
2.6%
D17506
 
2.6%
C17494
 
2.6%
U17485
 
2.6%
217482
 
2.6%
517479
 
2.6%
Other values (26)448897
66.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)676000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T69433
 
10.3%
B17579
 
2.6%
R17565
 
2.6%
917565
 
2.6%
I17515
 
2.6%
D17506
 
2.6%
C17494
 
2.6%
U17485
 
2.6%
217482
 
2.6%
517479
 
2.6%
Other values (26)448897
66.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)676000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T69433
 
10.3%
B17579
 
2.6%
R17565
 
2.6%
917565
 
2.6%
I17515
 
2.6%
D17506
 
2.6%
C17494
 
2.6%
U17485
 
2.6%
217482
 
2.6%
517479
 
2.6%
Other values (26)448897
66.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)676000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T69433
 
10.3%
B17579
 
2.6%
R17565
 
2.6%
917565
 
2.6%
I17515
 
2.6%
D17506
 
2.6%
C17494
 
2.6%
U17485
 
2.6%
217482
 
2.6%
517479
 
2.6%
Other values (26)448897
66.4%
Distinct51837
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size406.4 KiB
Minimum2025-07-01 00:09:59
Maximum2025-10-04 23:58:57
Invalid dates0
Invalid dates (%)0.0%
2026-02-07T11:28:18.066917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:18.132530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

transaction_type
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
PIX
31547 
Card
17930 
Wire
 
2523

Length

Max length4
Median length3
Mean length3.3933269
Min length3

Characters and Unicode

Total characters176453
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPIX
2nd rowPIX
3rd rowPIX
4th rowPIX
5th rowPIX

Common Values

ValueCountFrequency (%)
PIX31547
60.7%
Card17930
34.5%
Wire2523
 
4.9%

Length

2026-02-07T11:28:18.187356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-07T11:28:18.227781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pix31547
60.7%
card17930
34.5%
wire2523
 
4.9%

Most occurring characters

ValueCountFrequency (%)
P31547
17.9%
I31547
17.9%
X31547
17.9%
r20453
11.6%
C17930
10.2%
a17930
10.2%
d17930
10.2%
W2523
 
1.4%
i2523
 
1.4%
e2523
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)176453
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P31547
17.9%
I31547
17.9%
X31547
17.9%
r20453
11.6%
C17930
10.2%
a17930
10.2%
d17930
10.2%
W2523
 
1.4%
i2523
 
1.4%
e2523
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)176453
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P31547
17.9%
I31547
17.9%
X31547
17.9%
r20453
11.6%
C17930
10.2%
a17930
10.2%
d17930
10.2%
W2523
 
1.4%
i2523
 
1.4%
e2523
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)176453
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P31547
17.9%
I31547
17.9%
X31547
17.9%
r20453
11.6%
C17930
10.2%
a17930
10.2%
d17930
10.2%
W2523
 
1.4%
i2523
 
1.4%
e2523
 
1.4%
Distinct3497
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2026-02-07T11:28:18.399174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters364000
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)0.1%

Sample

1st rowC100602
2nd rowC101190
3rd rowC101811
4th rowC100023
5th rowM200612
ValueCountFrequency (%)
c10134136
 
0.1%
c10059035
 
0.1%
c10093233
 
0.1%
c10104832
 
0.1%
c10164732
 
0.1%
c10187432
 
0.1%
c10061131
 
0.1%
c10234831
 
0.1%
c10041830
 
0.1%
c10027430
 
0.1%
Other values (3487)51678
99.4%
2026-02-07T11:28:18.632042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
092386
25.4%
181810
22.5%
C46760
12.8%
231222
 
8.6%
416695
 
4.6%
316541
 
4.5%
714916
 
4.1%
614768
 
4.1%
814626
 
4.0%
514568
 
4.0%
Other values (2)19708
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)364000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
092386
25.4%
181810
22.5%
C46760
12.8%
231222
 
8.6%
416695
 
4.6%
316541
 
4.5%
714916
 
4.1%
614768
 
4.1%
814626
 
4.0%
514568
 
4.0%
Other values (2)19708
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)364000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
092386
25.4%
181810
22.5%
C46760
12.8%
231222
 
8.6%
416695
 
4.6%
316541
 
4.5%
714916
 
4.1%
614768
 
4.1%
814626
 
4.0%
514568
 
4.0%
Other values (2)19708
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)364000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
092386
25.4%
181810
22.5%
C46760
12.8%
231222
 
8.6%
416695
 
4.6%
316541
 
4.5%
714916
 
4.1%
614768
 
4.1%
814626
 
4.0%
514568
 
4.0%
Other values (2)19708
 
5.4%

sender_entity_type
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
customer
46760 
merchant
5240 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters416000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcustomer
2nd rowcustomer
3rd rowcustomer
4th rowcustomer
5th rowmerchant

Common Values

ValueCountFrequency (%)
customer46760
89.9%
merchant5240
 
10.1%

Length

2026-02-07T11:28:18.697361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-07T11:28:18.732265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
customer46760
89.9%
merchant5240
 
10.1%

Most occurring characters

ValueCountFrequency (%)
c52000
12.5%
m52000
12.5%
t52000
12.5%
e52000
12.5%
r52000
12.5%
s46760
11.2%
u46760
11.2%
o46760
11.2%
h5240
 
1.3%
a5240
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)416000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c52000
12.5%
m52000
12.5%
t52000
12.5%
e52000
12.5%
r52000
12.5%
s46760
11.2%
u46760
11.2%
o46760
11.2%
h5240
 
1.3%
a5240
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)416000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c52000
12.5%
m52000
12.5%
t52000
12.5%
e52000
12.5%
r52000
12.5%
s46760
11.2%
u46760
11.2%
o46760
11.2%
h5240
 
1.3%
a5240
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)416000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c52000
12.5%
m52000
12.5%
t52000
12.5%
e52000
12.5%
r52000
12.5%
s46760
11.2%
u46760
11.2%
o46760
11.2%
h5240
 
1.3%
a5240
 
1.3%
Distinct3485
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2026-02-07T11:28:18.897624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters364000
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)0.1%

Sample

1st rowM200223
2nd rowM200471
3rd rowC101070
4th rowC101820
5th rowC101267
ValueCountFrequency (%)
m20076159
 
0.1%
m20014358
 
0.1%
m20042058
 
0.1%
m20038558
 
0.1%
m20074856
 
0.1%
m20032456
 
0.1%
m20022455
 
0.1%
m20097255
 
0.1%
m20033854
 
0.1%
m20007554
 
0.1%
Other values (3475)51437
98.9%
2026-02-07T11:28:19.115078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0112052
30.8%
257425
15.8%
M39170
 
10.8%
133814
 
9.3%
415996
 
4.4%
315957
 
4.4%
815423
 
4.2%
515421
 
4.2%
715398
 
4.2%
615332
 
4.2%
Other values (2)28012
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)364000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0112052
30.8%
257425
15.8%
M39170
 
10.8%
133814
 
9.3%
415996
 
4.4%
315957
 
4.4%
815423
 
4.2%
515421
 
4.2%
715398
 
4.2%
615332
 
4.2%
Other values (2)28012
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)364000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0112052
30.8%
257425
15.8%
M39170
 
10.8%
133814
 
9.3%
415996
 
4.4%
315957
 
4.4%
815423
 
4.2%
515421
 
4.2%
715398
 
4.2%
615332
 
4.2%
Other values (2)28012
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)364000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0112052
30.8%
257425
15.8%
M39170
 
10.8%
133814
 
9.3%
415996
 
4.4%
315957
 
4.4%
815423
 
4.2%
515421
 
4.2%
715398
 
4.2%
615332
 
4.2%
Other values (2)28012
 
7.7%

receiver_entity_type
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
merchant
39170 
customer
12830 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters416000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmerchant
2nd rowmerchant
3rd rowcustomer
4th rowcustomer
5th rowcustomer

Common Values

ValueCountFrequency (%)
merchant39170
75.3%
customer12830
 
24.7%

Length

2026-02-07T11:28:19.169518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-07T11:28:19.202197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
merchant39170
75.3%
customer12830
 
24.7%

Most occurring characters

ValueCountFrequency (%)
m52000
12.5%
e52000
12.5%
r52000
12.5%
c52000
12.5%
t52000
12.5%
a39170
9.4%
h39170
9.4%
n39170
9.4%
u12830
 
3.1%
s12830
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)416000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m52000
12.5%
e52000
12.5%
r52000
12.5%
c52000
12.5%
t52000
12.5%
a39170
9.4%
h39170
9.4%
n39170
9.4%
u12830
 
3.1%
s12830
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)416000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m52000
12.5%
e52000
12.5%
r52000
12.5%
c52000
12.5%
t52000
12.5%
a39170
9.4%
h39170
9.4%
n39170
9.4%
u12830
 
3.1%
s12830
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)416000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m52000
12.5%
e52000
12.5%
r52000
12.5%
c52000
12.5%
t52000
12.5%
a39170
9.4%
h39170
9.4%
n39170
9.4%
u12830
 
3.1%
s12830
 
3.1%

amount_brl
Real number (ℝ)

High correlation 

Distinct50201
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4424.1896
Minimum26.07
Maximum140910.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size406.4 KiB
2026-02-07T11:28:19.250573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26.07
5-th percentile526.137
Q11376.1975
median2685.665
Q35268.2475
95-th percentile13792.631
Maximum140910.35
Range140884.28
Interquartile range (IQR)3892.05

Descriptive statistics

Standard deviation5669.3072
Coefficient of variation (CV)1.2814341
Kurtosis48.104187
Mean4424.1896
Median Absolute Deviation (MAD)1599.86
Skewness4.9509244
Sum2.3005786 × 108
Variance32141044
MonotonicityNot monotonic
2026-02-07T11:28:19.309979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2872.364
 
< 0.1%
1395.33
 
< 0.1%
2593.243
 
< 0.1%
2009.853
 
< 0.1%
1525.53
 
< 0.1%
1334.413
 
< 0.1%
2617.043
 
< 0.1%
1427.753
 
< 0.1%
1502.53
 
< 0.1%
3063.563
 
< 0.1%
Other values (50191)51969
99.9%
ValueCountFrequency (%)
26.071
< 0.1%
44.861
< 0.1%
48.731
< 0.1%
51.651
< 0.1%
51.781
< 0.1%
53.21
< 0.1%
53.391
< 0.1%
60.471
< 0.1%
63.141
< 0.1%
63.981
< 0.1%
ValueCountFrequency (%)
140910.351
< 0.1%
130114.241
< 0.1%
125782.451
< 0.1%
116387.081
< 0.1%
106455.071
< 0.1%
103439.381
< 0.1%
102037.121
< 0.1%
84323.521
< 0.1%
82628.681
< 0.1%
79147.971
< 0.1%

amount_orig
Real number (ℝ)

High correlation 

Distinct50191
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4278.8679
Minimum23.12
Maximum130114.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size406.4 KiB
2026-02-07T11:28:19.367830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23.12
5-th percentile433.099
Q11275.95
median2563.795
Q35100.9775
95-th percentile13558.234
Maximum130114.24
Range130091.12
Interquartile range (IQR)3825.0275

Descriptive statistics

Standard deviation5584.4542
Coefficient of variation (CV)1.3051242
Kurtosis44.139836
Mean4278.8679
Median Absolute Deviation (MAD)1581.145
Skewness4.833793
Sum2.2250113 × 108
Variance31186129
MonotonicityNot monotonic
2026-02-07T11:28:19.433839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2872.364
 
< 0.1%
2043.113
 
< 0.1%
2009.853
 
< 0.1%
1275.913
 
< 0.1%
1160.553
 
< 0.1%
616.153
 
< 0.1%
1427.753
 
< 0.1%
325.253
 
< 0.1%
1254.463
 
< 0.1%
2429.793
 
< 0.1%
Other values (50181)51969
99.9%
ValueCountFrequency (%)
23.121
< 0.1%
26.071
< 0.1%
27.061
< 0.1%
28.131
< 0.1%
28.61
< 0.1%
29.011
< 0.1%
29.161
< 0.1%
29.241
< 0.1%
30.951
< 0.1%
31.221
< 0.1%
ValueCountFrequency (%)
130114.241
< 0.1%
125782.451
< 0.1%
116387.081
< 0.1%
106455.071
< 0.1%
103439.381
< 0.1%
102037.121
< 0.1%
84323.521
< 0.1%
82628.681
< 0.1%
79147.971
< 0.1%
76914.461
< 0.1%

currency
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
BRL
49893 
USD
 
1067
EUR
 
1040

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters156000
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBRL
2nd rowBRL
3rd rowBRL
4th rowBRL
5th rowBRL

Common Values

ValueCountFrequency (%)
BRL49893
95.9%
USD1067
 
2.1%
EUR1040
 
2.0%

Length

2026-02-07T11:28:19.488928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-07T11:28:19.527672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
brl49893
95.9%
usd1067
 
2.1%
eur1040
 
2.0%

Most occurring characters

ValueCountFrequency (%)
R50933
32.6%
B49893
32.0%
L49893
32.0%
U2107
 
1.4%
S1067
 
0.7%
D1067
 
0.7%
E1040
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)156000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R50933
32.6%
B49893
32.0%
L49893
32.0%
U2107
 
1.4%
S1067
 
0.7%
D1067
 
0.7%
E1040
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)156000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R50933
32.6%
B49893
32.0%
L49893
32.0%
U2107
 
1.4%
S1067
 
0.7%
D1067
 
0.7%
E1040
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)156000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R50933
32.6%
B49893
32.0%
L49893
32.0%
U2107
 
1.4%
S1067
 
0.7%
D1067
 
0.7%
E1040
 
0.7%

fx_to_brl
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
1
49893 
5
 
1067
6
 
1040

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters52000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
149893
95.9%
51067
 
2.1%
61040
 
2.0%

Length

2026-02-07T11:28:19.568837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-07T11:28:19.609782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
149893
95.9%
51067
 
2.1%
61040
 
2.0%

Most occurring characters

ValueCountFrequency (%)
149893
95.9%
51067
 
2.1%
61040
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)52000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
149893
95.9%
51067
 
2.1%
61040
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)52000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
149893
95.9%
51067
 
2.1%
61040
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)52000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
149893
95.9%
51067
 
2.1%
61040
 
2.0%

status
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
Confirmed
47796 
Pending
 
2063
Failed
 
1071
Chargeback
 
1070

Length

Max length10
Median length9
Mean length8.8794423
Min length6

Characters and Unicode

Total characters461731
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConfirmed
2nd rowConfirmed
3rd rowConfirmed
4th rowConfirmed
5th rowPending

Common Values

ValueCountFrequency (%)
Confirmed47796
91.9%
Pending2063
 
4.0%
Failed1071
 
2.1%
Chargeback1070
 
2.1%

Length

2026-02-07T11:28:19.648573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-07T11:28:19.684695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
confirmed47796
91.9%
pending2063
 
4.0%
failed1071
 
2.1%
chargeback1070
 
2.1%

Most occurring characters

ValueCountFrequency (%)
e52000
11.3%
n51922
11.2%
d50930
11.0%
i50930
11.0%
r48866
10.6%
C48866
10.6%
o47796
10.4%
f47796
10.4%
m47796
10.4%
a3211
 
0.7%
Other values (8)11618
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)461731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e52000
11.3%
n51922
11.2%
d50930
11.0%
i50930
11.0%
r48866
10.6%
C48866
10.6%
o47796
10.4%
f47796
10.4%
m47796
10.4%
a3211
 
0.7%
Other values (8)11618
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)461731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e52000
11.3%
n51922
11.2%
d50930
11.0%
i50930
11.0%
r48866
10.6%
C48866
10.6%
o47796
10.4%
f47796
10.4%
m47796
10.4%
a3211
 
0.7%
Other values (8)11618
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)461731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e52000
11.3%
n51922
11.2%
d50930
11.0%
i50930
11.0%
r48866
10.6%
C48866
10.6%
o47796
10.4%
f47796
10.4%
m47796
10.4%
a3211
 
0.7%
Other values (8)11618
 
2.5%

channel
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
App
20893 
API
20843 
Terminal
5156 
Web
5108 

Length

Max length8
Median length3
Mean length3.4957692
Min length3

Characters and Unicode

Total characters181780
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApp
2nd rowApp
3rd rowApp
4th rowApp
5th rowAPI

Common Values

ValueCountFrequency (%)
App20893
40.2%
API20843
40.1%
Terminal5156
 
9.9%
Web5108
 
9.8%

Length

2026-02-07T11:28:19.725909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-07T11:28:19.759645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
app20893
40.2%
api20843
40.1%
terminal5156
 
9.9%
web5108
 
9.8%

Most occurring characters

ValueCountFrequency (%)
p41786
23.0%
A41736
23.0%
P20843
11.5%
I20843
11.5%
e10264
 
5.6%
T5156
 
2.8%
r5156
 
2.8%
m5156
 
2.8%
i5156
 
2.8%
n5156
 
2.8%
Other values (4)20528
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)181780
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p41786
23.0%
A41736
23.0%
P20843
11.5%
I20843
11.5%
e10264
 
5.6%
T5156
 
2.8%
r5156
 
2.8%
m5156
 
2.8%
i5156
 
2.8%
n5156
 
2.8%
Other values (4)20528
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)181780
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p41786
23.0%
A41736
23.0%
P20843
11.5%
I20843
11.5%
e10264
 
5.6%
T5156
 
2.8%
r5156
 
2.8%
m5156
 
2.8%
i5156
 
2.8%
n5156
 
2.8%
Other values (4)20528
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)181780
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p41786
23.0%
A41736
23.0%
P20843
11.5%
I20843
11.5%
e10264
 
5.6%
T5156
 
2.8%
r5156
 
2.8%
m5156
 
2.8%
i5156
 
2.8%
n5156
 
2.8%
Other values (4)20528
11.3%

capture_method
Categorical

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
QR Dynamic
7956 
CopyPaste
7946 
QR Static
7859 
Pix Key
7786 
Magstripe
3655 
Other values (6)
16798 

Length

Max length10
Median length9
Mean length7.6961731
Min length3

Characters and Unicode

Total characters400201
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCopyPaste
2nd rowPix Key
3rd rowCopyPaste
4th rowPix Key
5th rowQR Static

Common Values

ValueCountFrequency (%)
QR Dynamic7956
15.3%
CopyPaste7946
15.3%
QR Static7859
15.1%
Pix Key7786
15.0%
Magstripe3655
7.0%
Chip3612
6.9%
MOTO3579
6.9%
NFC3568
6.9%
E-commerce3516
6.8%
SWIFT1271
 
2.4%

Length

2026-02-07T11:28:19.816001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
qr15815
20.9%
dynamic7956
10.5%
copypaste7946
10.5%
static7859
10.4%
pix7786
10.3%
key7786
10.3%
magstripe3655
 
4.8%
chip3612
 
4.8%
moto3579
 
4.7%
nfc3568
 
4.7%
Other values (3)6039
 
8.0%

Most occurring characters

ValueCountFrequency (%)
i32120
 
8.0%
t28571
 
7.1%
e27671
 
6.9%
a27416
 
6.9%
c24099
 
6.0%
y23688
 
5.9%
23601
 
5.9%
m16240
 
4.1%
R15815
 
4.0%
Q15815
 
4.0%
Other values (22)165165
41.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)400201
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i32120
 
8.0%
t28571
 
7.1%
e27671
 
6.9%
a27416
 
6.9%
c24099
 
6.0%
y23688
 
5.9%
23601
 
5.9%
m16240
 
4.1%
R15815
 
4.0%
Q15815
 
4.0%
Other values (22)165165
41.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)400201
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i32120
 
8.0%
t28571
 
7.1%
e27671
 
6.9%
a27416
 
6.9%
c24099
 
6.0%
y23688
 
5.9%
23601
 
5.9%
m16240
 
4.1%
R15815
 
4.0%
Q15815
 
4.0%
Other values (22)165165
41.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)400201
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i32120
 
8.0%
t28571
 
7.1%
e27671
 
6.9%
a27416
 
6.9%
c24099
 
6.0%
y23688
 
5.9%
23601
 
5.9%
m16240
 
4.1%
R15815
 
4.0%
Q15815
 
4.0%
Other values (22)165165
41.3%

payment_method
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing34070
Missing (%)65.5%
Memory size2.8 MiB
credit
9017 
debit
8913 

Length

Max length6
Median length6
Mean length5.5029002
Min length5

Characters and Unicode

Total characters98667
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit
2nd rowcredit
3rd rowcredit
4th rowcredit
5th rowcredit

Common Values

ValueCountFrequency (%)
credit9017
 
17.3%
debit8913
 
17.1%
(Missing)34070
65.5%

Length

2026-02-07T11:28:19.865605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-07T11:28:19.895098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
credit9017
50.3%
debit8913
49.7%

Most occurring characters

ValueCountFrequency (%)
e17930
18.2%
i17930
18.2%
d17930
18.2%
t17930
18.2%
c9017
9.1%
r9017
9.1%
b8913
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)98667
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e17930
18.2%
i17930
18.2%
d17930
18.2%
t17930
18.2%
c9017
9.1%
r9017
9.1%
b8913
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)98667
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e17930
18.2%
i17930
18.2%
d17930
18.2%
t17930
18.2%
c9017
9.1%
r9017
9.1%
b8913
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)98667
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e17930
18.2%
i17930
18.2%
d17930
18.2%
t17930
18.2%
c9017
9.1%
r9017
9.1%
b8913
9.0%

installments
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3503654
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size406.4 KiB
2026-02-07T11:28:19.925799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5343631
Coefficient of variation (CV)1.1362577
Kurtosis25.802538
Mean1.3503654
Median Absolute Deviation (MAD)0
Skewness5.0134083
Sum70219
Variance2.3542701
MonotonicityNot monotonic
2026-02-07T11:28:19.962496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
148283
92.9%
2713
 
1.4%
6698
 
1.3%
8561
 
1.1%
3546
 
1.1%
10452
 
0.9%
4411
 
0.8%
12336
 
0.6%
ValueCountFrequency (%)
148283
92.9%
2713
 
1.4%
3546
 
1.1%
4411
 
0.8%
6698
 
1.3%
8561
 
1.1%
10452
 
0.9%
12336
 
0.6%
ValueCountFrequency (%)
12336
 
0.6%
10452
 
0.9%
8561
 
1.1%
6698
 
1.3%
4411
 
0.8%
3546
 
1.1%
2713
 
1.4%
148283
92.9%

issuing_or_acquiring
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing34070
Missing (%)65.5%
Memory size2.8 MiB
acquiring
13466 
issuing
4464 

Length

Max length9
Median length9
Mean length8.5020636
Min length7

Characters and Unicode

Total characters152442
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowacquiring
2nd rowacquiring
3rd rowacquiring
4th rowissuing
5th rowissuing

Common Values

ValueCountFrequency (%)
acquiring13466
 
25.9%
issuing4464
 
8.6%
(Missing)34070
65.5%

Length

2026-02-07T11:28:20.012649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-07T11:28:20.047000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
acquiring13466
75.1%
issuing4464
 
24.9%

Most occurring characters

ValueCountFrequency (%)
i35860
23.5%
g17930
11.8%
u17930
11.8%
n17930
11.8%
a13466
 
8.8%
c13466
 
8.8%
q13466
 
8.8%
r13466
 
8.8%
s8928
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)152442
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i35860
23.5%
g17930
11.8%
u17930
11.8%
n17930
11.8%
a13466
 
8.8%
c13466
 
8.8%
q13466
 
8.8%
r13466
 
8.8%
s8928
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)152442
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i35860
23.5%
g17930
11.8%
u17930
11.8%
n17930
11.8%
a13466
 
8.8%
c13466
 
8.8%
q13466
 
8.8%
r13466
 
8.8%
s8928
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)152442
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i35860
23.5%
g17930
11.8%
u17930
11.8%
n17930
11.8%
a13466
 
8.8%
c13466
 
8.8%
q13466
 
8.8%
r13466
 
8.8%
s8928
 
5.9%

pix
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
True
31547 
False
20453 
ValueCountFrequency (%)
True31547
60.7%
False20453
39.3%
2026-02-07T11:28:20.075903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

pix_flow
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing20453
Missing (%)39.3%
Memory size2.8 MiB
cash_out
28373 
cash_in
3174 

Length

Max length8
Median length8
Mean length7.8993882
Min length7

Characters and Unicode

Total characters249202
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcash_out
2nd rowcash_out
3rd rowcash_out
4th rowcash_out
5th rowcash_in

Common Values

ValueCountFrequency (%)
cash_out28373
54.6%
cash_in3174
 
6.1%
(Missing)20453
39.3%

Length

2026-02-07T11:28:20.117455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-07T11:28:20.149725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cash_out28373
89.9%
cash_in3174
 
10.1%

Most occurring characters

ValueCountFrequency (%)
c31547
12.7%
a31547
12.7%
s31547
12.7%
h31547
12.7%
_31547
12.7%
o28373
11.4%
u28373
11.4%
t28373
11.4%
i3174
 
1.3%
n3174
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)249202
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c31547
12.7%
a31547
12.7%
s31547
12.7%
h31547
12.7%
_31547
12.7%
o28373
11.4%
u28373
11.4%
t28373
11.4%
i3174
 
1.3%
n3174
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)249202
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c31547
12.7%
a31547
12.7%
s31547
12.7%
h31547
12.7%
_31547
12.7%
o28373
11.4%
u28373
11.4%
t28373
11.4%
i3174
 
1.3%
n3174
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)249202
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c31547
12.7%
a31547
12.7%
s31547
12.7%
h31547
12.7%
_31547
12.7%
o28373
11.4%
u28373
11.4%
t28373
11.4%
i3174
 
1.3%
n3174
 
1.3%

card_brand
Categorical

High correlation  Missing 

Distinct5
Distinct (%)< 0.1%
Missing34070
Missing (%)65.5%
Memory size2.8 MiB
Visa
8041 
Mastercard
7156 
Amex
1124 
Elo
1063 
Hipercard
 
546

Length

Max length10
Median length4
Mean length6.4876185
Min length3

Characters and Unicode

Total characters116323
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVisa
2nd rowElo
3rd rowMastercard
4th rowVisa
5th rowVisa

Common Values

ValueCountFrequency (%)
Visa8041
 
15.5%
Mastercard7156
 
13.8%
Amex1124
 
2.2%
Elo1063
 
2.0%
Hipercard546
 
1.1%
(Missing)34070
65.5%

Length

2026-02-07T11:28:20.193161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-07T11:28:20.235486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
visa8041
44.8%
mastercard7156
39.9%
amex1124
 
6.3%
elo1063
 
5.9%
hipercard546
 
3.0%

Most occurring characters

ValueCountFrequency (%)
a22899
19.7%
r15404
13.2%
s15197
13.1%
e8826
 
7.6%
i8587
 
7.4%
V8041
 
6.9%
c7702
 
6.6%
d7702
 
6.6%
t7156
 
6.2%
M7156
 
6.2%
Other values (8)7653
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)116323
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a22899
19.7%
r15404
13.2%
s15197
13.1%
e8826
 
7.6%
i8587
 
7.4%
V8041
 
6.9%
c7702
 
6.6%
d7702
 
6.6%
t7156
 
6.2%
M7156
 
6.2%
Other values (8)7653
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)116323
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a22899
19.7%
r15404
13.2%
s15197
13.1%
e8826
 
7.6%
i8587
 
7.4%
V8041
 
6.9%
c7702
 
6.6%
d7702
 
6.6%
t7156
 
6.2%
M7156
 
6.2%
Other values (8)7653
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)116323
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a22899
19.7%
r15404
13.2%
s15197
13.1%
e8826
 
7.6%
i8587
 
7.4%
V8041
 
6.9%
c7702
 
6.6%
d7702
 
6.6%
t7156
 
6.2%
M7156
 
6.2%
Other values (8)7653
 
6.6%

card_present
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing34070
Missing (%)65.5%
Memory size101.7 KiB
True
10835 
False
7095 
(Missing)
34070 
ValueCountFrequency (%)
True10835
 
20.8%
False7095
 
13.6%
(Missing)34070
65.5%
2026-02-07T11:28:20.288821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

auth_3ds
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing44905
Missing (%)86.4%
Memory size101.7 KiB
False
4633 
True
 
2462
(Missing)
44905 
ValueCountFrequency (%)
False4633
 
8.9%
True2462
 
4.7%
(Missing)44905
86.4%
2026-02-07T11:28:20.322350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

eci
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing44905
Missing (%)86.4%
Memory size2.8 MiB
7.0
4728 
5.0
1184 
6.0
1183 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21285
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7.0
2nd row6.0
3rd row6.0
4th row7.0
5th row5.0

Common Values

ValueCountFrequency (%)
7.04728
 
9.1%
5.01184
 
2.3%
6.01183
 
2.3%
(Missing)44905
86.4%

Length

2026-02-07T11:28:20.390150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-07T11:28:20.461485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
7.04728
66.6%
5.01184
 
16.7%
6.01183
 
16.7%

Most occurring characters

ValueCountFrequency (%)
.7095
33.3%
07095
33.3%
74728
22.2%
51184
 
5.6%
61183
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)21285
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.7095
33.3%
07095
33.3%
74728
22.2%
51184
 
5.6%
61183
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21285
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.7095
33.3%
07095
33.3%
74728
22.2%
51184
 
5.6%
61183
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21285
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.7095
33.3%
07095
33.3%
74728
22.2%
51184
 
5.6%
61183
 
5.6%

mcc
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5596.0916
Minimum4111
Maximum7995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size406.4 KiB
2026-02-07T11:28:20.511745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4111
5-th percentile4111
Q14829
median5732
Q36011
95-th percentile7995
Maximum7995
Range3884
Interquartile range (IQR)1182

Descriptive statistics

Standard deviation880.74155
Coefficient of variation (CV)0.15738512
Kurtosis1.2758465
Mean5596.0916
Median Absolute Deviation (MAD)321
Skewness0.80401124
Sum2.9099676 × 108
Variance775705.68
MonotonicityNot monotonic
2026-02-07T11:28:20.582935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
59994377
 
8.4%
60514108
 
7.9%
48293966
 
7.6%
59453955
 
7.6%
48143950
 
7.6%
60113733
 
7.2%
41113726
 
7.2%
54113681
 
7.1%
47893672
 
7.1%
57323589
 
6.9%
Other values (4)13243
25.5%
ValueCountFrequency (%)
41113726
7.2%
47893672
7.1%
48143950
7.6%
48293966
7.6%
49003445
6.6%
54113681
7.1%
57323589
6.9%
58123167
6.1%
59453955
7.6%
59994377
8.4%
ValueCountFrequency (%)
79953306
6.4%
62113325
6.4%
60514108
7.9%
60113733
7.2%
59994377
8.4%
59453955
7.6%
58123167
6.1%
57323589
6.9%
54113681
7.1%
49003445
6.6%

geo_country
Categorical

High correlation  Imbalance 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
BR
41887 
US
 
2287
DE
 
1286
AE
 
1029
PT
 
920
Other values (15)
4591 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters104000
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBR
2nd rowBR
3rd rowBR
4th rowBR
5th rowBR

Common Values

ValueCountFrequency (%)
BR41887
80.6%
US2287
 
4.4%
DE1286
 
2.5%
AE1029
 
2.0%
PT920
 
1.8%
GB764
 
1.5%
ES756
 
1.5%
FR580
 
1.1%
CN547
 
1.1%
RU373
 
0.7%
Other values (10)1571
 
3.0%

Length

2026-02-07T11:28:20.662231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
br41887
80.6%
us2287
 
4.4%
de1286
 
2.5%
ae1029
 
2.0%
pt920
 
1.8%
gb764
 
1.5%
es756
 
1.5%
fr580
 
1.1%
cn547
 
1.1%
ru373
 
0.7%
Other values (10)1571
 
3.0%

Most occurring characters

ValueCountFrequency (%)
R43267
41.6%
B42816
41.2%
E3410
 
3.3%
S3128
 
3.0%
U2660
 
2.6%
A1447
 
1.4%
D1286
 
1.2%
P1110
 
1.1%
T920
 
0.9%
G764
 
0.7%
Other values (9)3192
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)104000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R43267
41.6%
B42816
41.2%
E3410
 
3.3%
S3128
 
3.0%
U2660
 
2.6%
A1447
 
1.4%
D1286
 
1.2%
P1110
 
1.1%
T920
 
0.9%
G764
 
0.7%
Other values (9)3192
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)104000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R43267
41.6%
B42816
41.2%
E3410
 
3.3%
S3128
 
3.0%
U2660
 
2.6%
A1447
 
1.4%
D1286
 
1.2%
P1110
 
1.1%
T920
 
0.9%
G764
 
0.7%
Other values (9)3192
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)104000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R43267
41.6%
B42816
41.2%
E3410
 
3.3%
S3128
 
3.0%
U2660
 
2.6%
A1447
 
1.4%
D1286
 
1.2%
P1110
 
1.1%
T920
 
0.9%
G764
 
0.7%
Other values (9)3192
 
3.1%

geolocation_lat
Real number (ℝ)

High correlation 

Distinct51973
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.1711939
Minimum-54.95725
Maximum81.906848
Zeros0
Zeros (%)0.0%
Negative36421
Negative (%)70.0%
Memory size406.4 KiB
2026-02-07T11:28:20.744552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-54.95725
5-th percentile-31.416694
Q1-21.830255
median-9.810809
Q32.3389425
95-th percentile48.367944
Maximum81.906848
Range136.8641
Interquartile range (IQR)24.169197

Descriptive statistics

Standard deviation24.088601
Coefficient of variation (CV)-5.7749894
Kurtosis0.36806882
Mean-4.1711939
Median Absolute Deviation (MAD)12.076955
Skewness1.0988841
Sum-216902.08
Variance580.26068
MonotonicityNot monotonic
2026-02-07T11:28:20.865514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.8765672
 
< 0.1%
-4.3341272
 
< 0.1%
39.1520632
 
< 0.1%
-29.3675252
 
< 0.1%
-0.4101592
 
< 0.1%
-23.0698562
 
< 0.1%
0.8120282
 
< 0.1%
-17.1983122
 
< 0.1%
-1.000982
 
< 0.1%
-29.5868942
 
< 0.1%
Other values (51963)51980
> 99.9%
ValueCountFrequency (%)
-54.957251
< 0.1%
-54.3929671
< 0.1%
-54.3006271
< 0.1%
-53.969351
< 0.1%
-53.9418281
< 0.1%
-53.928231
< 0.1%
-53.8252321
< 0.1%
-53.8187741
< 0.1%
-53.7967881
< 0.1%
-53.672981
< 0.1%
ValueCountFrequency (%)
81.9068481
< 0.1%
81.8534421
< 0.1%
81.8007991
< 0.1%
81.7473341
< 0.1%
81.6879581
< 0.1%
81.6557611
< 0.1%
81.5056851
< 0.1%
81.3746091
< 0.1%
81.339311
< 0.1%
81.3065391
< 0.1%

geolocation_lon
Real number (ℝ)

High correlation 

Distinct51977
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-44.021573
Minimum-124.99382
Maximum178.80549
Zeros0
Zeros (%)0.0%
Negative46796
Negative (%)90.0%
Memory size406.4 KiB
2026-02-07T11:28:20.953421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-124.99382
5-th percentile-73.337051
Q1-64.139658
median-52.212219
Q3-39.961913
95-th percentile47.149345
Maximum178.80549
Range303.79931
Interquartile range (IQR)24.177745

Descriptive statistics

Standard deviation37.010005
Coefficient of variation (CV)-0.84072427
Kurtosis6.8520364
Mean-44.021573
Median Absolute Deviation (MAD)12.094706
Skewness2.279144
Sum-2289121.8
Variance1369.7405
MonotonicityNot monotonic
2026-02-07T11:28:21.054631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-66.3852252
 
< 0.1%
-57.4331782
 
< 0.1%
-68.9426242
 
< 0.1%
-41.7425852
 
< 0.1%
-41.7593752
 
< 0.1%
-45.7948632
 
< 0.1%
-73.7639342
 
< 0.1%
-58.9739432
 
< 0.1%
-60.5675342
 
< 0.1%
-52.5906992
 
< 0.1%
Other values (51967)51980
> 99.9%
ValueCountFrequency (%)
-124.9938231
< 0.1%
-124.9808731
< 0.1%
-124.9744161
< 0.1%
-124.9697421
< 0.1%
-124.9472681
< 0.1%
-124.9292961
< 0.1%
-124.9161581
< 0.1%
-124.9034331
< 0.1%
-124.8348661
< 0.1%
-124.7929591
< 0.1%
ValueCountFrequency (%)
178.8054891
< 0.1%
178.7246991
< 0.1%
178.1038441
< 0.1%
177.9038791
< 0.1%
176.7343131
< 0.1%
176.7133011
< 0.1%
176.3724761
< 0.1%
175.467411
< 0.1%
175.0885281
< 0.1%
174.9707381
< 0.1%

ip_country
Categorical

High correlation  Imbalance 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
BR
50102 
US
 
296
DE
 
175
AE
 
170
PT
 
153
Other values (15)
 
1104

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters104000
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDE
2nd rowBR
3rd rowBR
4th rowBR
5th rowBR

Common Values

ValueCountFrequency (%)
BR50102
96.4%
US296
 
0.6%
DE175
 
0.3%
AE170
 
0.3%
PT153
 
0.3%
FR132
 
0.3%
GB119
 
0.2%
ES112
 
0.2%
CN99
 
0.2%
RU79
 
0.2%
Other values (10)563
 
1.1%

Length

2026-02-07T11:28:21.151789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
br50102
96.4%
us296
 
0.6%
de175
 
0.3%
ae170
 
0.3%
pt153
 
0.3%
fr132
 
0.3%
gb119
 
0.2%
es112
 
0.2%
cn99
 
0.2%
ru79
 
0.2%
Other values (10)563
 
1.1%

Most occurring characters

ValueCountFrequency (%)
R50429
48.5%
B50295
48.4%
E525
 
0.5%
S451
 
0.4%
U375
 
0.4%
A284
 
0.3%
Y233
 
0.2%
P213
 
0.2%
F182
 
0.2%
D175
 
0.2%
Other values (9)838
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)104000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R50429
48.5%
B50295
48.4%
E525
 
0.5%
S451
 
0.4%
U375
 
0.4%
A284
 
0.3%
Y233
 
0.2%
P213
 
0.2%
F182
 
0.2%
D175
 
0.2%
Other values (9)838
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)104000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R50429
48.5%
B50295
48.4%
E525
 
0.5%
S451
 
0.4%
U375
 
0.4%
A284
 
0.3%
Y233
 
0.2%
P213
 
0.2%
F182
 
0.2%
D175
 
0.2%
Other values (9)838
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)104000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R50429
48.5%
B50295
48.4%
E525
 
0.5%
S451
 
0.4%
U375
 
0.4%
A284
 
0.3%
Y233
 
0.2%
P213
 
0.2%
F182
 
0.2%
D175
 
0.2%
Other values (9)838
 
0.8%

ip_anomaly
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
False
51810 
True
 
190
ValueCountFrequency (%)
False51810
99.6%
True190
 
0.4%
2026-02-07T11:28:21.196095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

ip_proxy_vpn_tor
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.1%
Missing48337
Missing (%)93.0%
Memory size2.8 MiB
VPN
2124 
Proxy
996 
Tor
543 

Length

Max length5
Median length3
Mean length3.5438165
Min length3

Characters and Unicode

Total characters12981
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVPN
2nd rowVPN
3rd rowVPN
4th rowVPN
5th rowVPN

Common Values

ValueCountFrequency (%)
VPN2124
 
4.1%
Proxy996
 
1.9%
Tor543
 
1.0%
(Missing)48337
93.0%

Length

2026-02-07T11:28:21.250782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-07T11:28:21.310169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
vpn2124
58.0%
proxy996
27.2%
tor543
 
14.8%

Most occurring characters

ValueCountFrequency (%)
P3120
24.0%
V2124
16.4%
N2124
16.4%
r1539
11.9%
o1539
11.9%
x996
 
7.7%
y996
 
7.7%
T543
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)12981
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P3120
24.0%
V2124
16.4%
N2124
16.4%
r1539
11.9%
o1539
11.9%
x996
 
7.7%
y996
 
7.7%
T543
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12981
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P3120
24.0%
V2124
16.4%
N2124
16.4%
r1539
11.9%
o1539
11.9%
x996
 
7.7%
y996
 
7.7%
T543
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12981
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P3120
24.0%
V2124
16.4%
N2124
16.4%
r1539
11.9%
o1539
11.9%
x996
 
7.7%
y996
 
7.7%
T543
 
4.2%

device_fingerprint
Text

Unique 

Distinct52000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2026-02-07T11:28:21.544976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters832000
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52000 ?
Unique (%)100.0%

Sample

1st rowpoq50ptmz8g3pgoh
2nd rowve5ar34n3q5arhcn
3rd rowbnv7ad7zrmn7cgfh
4th rowh96qnwm3yju8wdd9
5th rowelmh8kqwykznkcfz
ValueCountFrequency (%)
poq50ptmz8g3pgoh1
 
< 0.1%
ve5ar34n3q5arhcn1
 
< 0.1%
bnv7ad7zrmn7cgfh1
 
< 0.1%
h96qnwm3yju8wdd91
 
< 0.1%
elmh8kqwykznkcfz1
 
< 0.1%
uv62ap2zve3i4v0r1
 
< 0.1%
jl2cwglejty90qvt1
 
< 0.1%
x10217gdx1i4hbeu1
 
< 0.1%
b3od13zwwn6ar81d1
 
< 0.1%
1qyg9doxytdb9quq1
 
< 0.1%
Other values (51990)51990
> 99.9%
2026-02-07T11:28:21.758668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t23373
 
2.8%
g23343
 
2.8%
723341
 
2.8%
h23313
 
2.8%
923287
 
2.8%
123268
 
2.8%
n23219
 
2.8%
s23210
 
2.8%
c23193
 
2.8%
j23179
 
2.8%
Other values (26)599274
72.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)832000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t23373
 
2.8%
g23343
 
2.8%
723341
 
2.8%
h23313
 
2.8%
923287
 
2.8%
123268
 
2.8%
n23219
 
2.8%
s23210
 
2.8%
c23193
 
2.8%
j23179
 
2.8%
Other values (26)599274
72.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)832000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t23373
 
2.8%
g23343
 
2.8%
723341
 
2.8%
h23313
 
2.8%
923287
 
2.8%
123268
 
2.8%
n23219
 
2.8%
s23210
 
2.8%
c23193
 
2.8%
j23179
 
2.8%
Other values (26)599274
72.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)832000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t23373
 
2.8%
g23343
 
2.8%
723341
 
2.8%
h23313
 
2.8%
923287
 
2.8%
123268
 
2.8%
n23219
 
2.8%
s23210
 
2.8%
c23193
 
2.8%
j23179
 
2.8%
Other values (26)599274
72.0%

ip_address
Text

Unique 

Distinct52000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
2026-02-07T11:28:21.904559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.282462
Min length8

Characters and Unicode

Total characters690688
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52000 ?
Unique (%)100.0%

Sample

1st row193.59.218.134
2nd row118.247.245.154
3rd row169.185.50.15
4th row150.97.162.214
5th row23.56.92.120
ValueCountFrequency (%)
193.59.218.1341
 
< 0.1%
118.247.245.1541
 
< 0.1%
169.185.50.151
 
< 0.1%
150.97.162.2141
 
< 0.1%
23.56.92.1201
 
< 0.1%
254.40.197.2051
 
< 0.1%
197.246.64.191
 
< 0.1%
159.199.221.411
 
< 0.1%
155.240.142.231
 
< 0.1%
140.177.222.1631
 
< 0.1%
Other values (51990)51990
> 99.9%
2026-02-07T11:28:22.095266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
.156000
22.6%
1126740
18.3%
290859
13.2%
345613
 
6.6%
445540
 
6.6%
542254
 
6.1%
037107
 
5.4%
736746
 
5.3%
936729
 
5.3%
836673
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)690688
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.156000
22.6%
1126740
18.3%
290859
13.2%
345613
 
6.6%
445540
 
6.6%
542254
 
6.1%
037107
 
5.4%
736746
 
5.3%
936729
 
5.3%
836673
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)690688
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.156000
22.6%
1126740
18.3%
290859
13.2%
345613
 
6.6%
445540
 
6.6%
542254
 
6.1%
037107
 
5.4%
736746
 
5.3%
936729
 
5.3%
836673
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)690688
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.156000
22.6%
1126740
18.3%
290859
13.2%
345613
 
6.6%
445540
 
6.6%
542254
 
6.1%
037107
 
5.4%
736746
 
5.3%
936729
 
5.3%
836673
 
5.3%

device_rooted
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
False
50438 
True
 
1562
ValueCountFrequency (%)
False50438
97.0%
True1562
 
3.0%
2026-02-07T11:28:22.134453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

sender_country
Categorical

High correlation  Imbalance 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
BR
50826 
US
 
254
DE
 
143
AE
 
121
PT
 
113
Other values (15)
 
543

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters104000
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBR
2nd rowBR
3rd rowBR
4th rowBR
5th rowBR

Common Values

ValueCountFrequency (%)
BR50826
97.7%
US254
 
0.5%
DE143
 
0.3%
AE121
 
0.2%
PT113
 
0.2%
FR90
 
0.2%
GB86
 
0.2%
ES82
 
0.2%
CN65
 
0.1%
YE39
 
0.1%
Other values (10)181
 
0.3%

Length

2026-02-07T11:28:22.184402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
br50826
97.7%
us254
 
0.5%
de143
 
0.3%
ae121
 
0.2%
pt113
 
0.2%
fr90
 
0.2%
gb86
 
0.2%
es82
 
0.2%
cn65
 
0.1%
ye39
 
0.1%
Other values (10)181
 
0.3%

Most occurring characters

ValueCountFrequency (%)
R50998
49.0%
B50929
49.0%
E385
 
0.4%
S342
 
0.3%
U290
 
0.3%
A166
 
0.2%
D143
 
0.1%
P135
 
0.1%
T113
 
0.1%
F104
 
0.1%
Other values (9)395
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)104000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R50998
49.0%
B50929
49.0%
E385
 
0.4%
S342
 
0.3%
U290
 
0.3%
A166
 
0.2%
D143
 
0.1%
P135
 
0.1%
T113
 
0.1%
F104
 
0.1%
Other values (9)395
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)104000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R50998
49.0%
B50929
49.0%
E385
 
0.4%
S342
 
0.3%
U290
 
0.3%
A166
 
0.2%
D143
 
0.1%
P135
 
0.1%
T113
 
0.1%
F104
 
0.1%
Other values (9)395
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)104000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R50998
49.0%
B50929
49.0%
E385
 
0.4%
S342
 
0.3%
U290
 
0.3%
A166
 
0.2%
D143
 
0.1%
P135
 
0.1%
T113
 
0.1%
F104
 
0.1%
Other values (9)395
 
0.4%

receiver_country
Categorical

High correlation  Imbalance 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
BR
43061 
US
 
2033
DE
 
1143
AE
 
908
PT
 
807
Other values (15)
 
4048

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters104000
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBR
2nd rowBR
3rd rowBR
4th rowBR
5th rowBR

Common Values

ValueCountFrequency (%)
BR43061
82.8%
US2033
 
3.9%
DE1143
 
2.2%
AE908
 
1.7%
PT807
 
1.6%
GB678
 
1.3%
ES674
 
1.3%
FR490
 
0.9%
CN482
 
0.9%
RU337
 
0.6%
Other values (10)1387
 
2.7%

Length

2026-02-07T11:28:22.236099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
br43061
82.8%
us2033
 
3.9%
de1143
 
2.2%
ae908
 
1.7%
pt807
 
1.6%
gb678
 
1.3%
es674
 
1.3%
fr490
 
0.9%
cn482
 
0.9%
ru337
 
0.6%
Other values (10)1387
 
2.7%

Most occurring characters

ValueCountFrequency (%)
R44269
42.6%
B43887
42.2%
E3025
 
2.9%
S2786
 
2.7%
U2370
 
2.3%
A1281
 
1.2%
D1143
 
1.1%
P975
 
0.9%
T807
 
0.8%
G678
 
0.7%
Other values (9)2779
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)104000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R44269
42.6%
B43887
42.2%
E3025
 
2.9%
S2786
 
2.7%
U2370
 
2.3%
A1281
 
1.2%
D1143
 
1.1%
P975
 
0.9%
T807
 
0.8%
G678
 
0.7%
Other values (9)2779
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)104000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R44269
42.6%
B43887
42.2%
E3025
 
2.9%
S2786
 
2.7%
U2370
 
2.3%
A1281
 
1.2%
D1143
 
1.1%
P975
 
0.9%
T807
 
0.8%
G678
 
0.7%
Other values (9)2779
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)104000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R44269
42.6%
B43887
42.2%
E3025
 
2.9%
S2786
 
2.7%
U2370
 
2.3%
A1281
 
1.2%
D1143
 
1.1%
P975
 
0.9%
T807
 
0.8%
G678
 
0.7%
Other values (9)2779
 
2.7%

country_risk_geo
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Low
50392 
Monitored
 
1018
High
 
590

Length

Max length9
Median length3
Mean length3.1288077
Min length3

Characters and Unicode

Total characters162698
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowLow
3rd rowLow
4th rowLow
5th rowLow

Common Values

ValueCountFrequency (%)
Low50392
96.9%
Monitored1018
 
2.0%
High590
 
1.1%

Length

2026-02-07T11:28:22.286682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-07T11:28:22.317312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
low50392
96.9%
monitored1018
 
2.0%
high590
 
1.1%

Most occurring characters

ValueCountFrequency (%)
o52428
32.2%
L50392
31.0%
w50392
31.0%
i1608
 
1.0%
M1018
 
0.6%
n1018
 
0.6%
t1018
 
0.6%
r1018
 
0.6%
e1018
 
0.6%
d1018
 
0.6%
Other values (3)1770
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)162698
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o52428
32.2%
L50392
31.0%
w50392
31.0%
i1608
 
1.0%
M1018
 
0.6%
n1018
 
0.6%
t1018
 
0.6%
r1018
 
0.6%
e1018
 
0.6%
d1018
 
0.6%
Other values (3)1770
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)162698
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o52428
32.2%
L50392
31.0%
w50392
31.0%
i1608
 
1.0%
M1018
 
0.6%
n1018
 
0.6%
t1018
 
0.6%
r1018
 
0.6%
e1018
 
0.6%
d1018
 
0.6%
Other values (3)1770
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)162698
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o52428
32.2%
L50392
31.0%
w50392
31.0%
i1608
 
1.0%
M1018
 
0.6%
n1018
 
0.6%
t1018
 
0.6%
r1018
 
0.6%
e1018
 
0.6%
d1018
 
0.6%
Other values (3)1770
 
1.1%

country_risk_ip
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Low
51422 
Monitored
 
310
High
 
268

Length

Max length9
Median length3
Mean length3.0409231
Min length3

Characters and Unicode

Total characters158128
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowLow
3rd rowLow
4th rowLow
5th rowLow

Common Values

ValueCountFrequency (%)
Low51422
98.9%
Monitored310
 
0.6%
High268
 
0.5%

Length

2026-02-07T11:28:22.360622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-07T11:28:22.392670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
low51422
98.9%
monitored310
 
0.6%
high268
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o52042
32.9%
L51422
32.5%
w51422
32.5%
i578
 
0.4%
M310
 
0.2%
n310
 
0.2%
t310
 
0.2%
r310
 
0.2%
e310
 
0.2%
d310
 
0.2%
Other values (3)804
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)158128
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o52042
32.9%
L51422
32.5%
w51422
32.5%
i578
 
0.4%
M310
 
0.2%
n310
 
0.2%
t310
 
0.2%
r310
 
0.2%
e310
 
0.2%
d310
 
0.2%
Other values (3)804
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)158128
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o52042
32.9%
L51422
32.5%
w51422
32.5%
i578
 
0.4%
M310
 
0.2%
n310
 
0.2%
t310
 
0.2%
r310
 
0.2%
e310
 
0.2%
d310
 
0.2%
Other values (3)804
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)158128
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o52042
32.9%
L51422
32.5%
w51422
32.5%
i578
 
0.4%
M310
 
0.2%
n310
 
0.2%
t310
 
0.2%
r310
 
0.2%
e310
 
0.2%
d310
 
0.2%
Other values (3)804
 
0.5%

country_risk_sender
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Low
51811 
Monitored
 
114
High
 
75

Length

Max length9
Median length3
Mean length3.0145962
Min length3

Characters and Unicode

Total characters156759
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowLow
3rd rowLow
4th rowLow
5th rowLow

Common Values

ValueCountFrequency (%)
Low51811
99.6%
Monitored114
 
0.2%
High75
 
0.1%

Length

2026-02-07T11:28:22.439161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-07T11:28:22.641454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
low51811
99.6%
monitored114
 
0.2%
high75
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o52039
33.2%
L51811
33.1%
w51811
33.1%
i189
 
0.1%
M114
 
0.1%
n114
 
0.1%
t114
 
0.1%
r114
 
0.1%
e114
 
0.1%
d114
 
0.1%
Other values (3)225
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)156759
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o52039
33.2%
L51811
33.1%
w51811
33.1%
i189
 
0.1%
M114
 
0.1%
n114
 
0.1%
t114
 
0.1%
r114
 
0.1%
e114
 
0.1%
d114
 
0.1%
Other values (3)225
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)156759
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o52039
33.2%
L51811
33.1%
w51811
33.1%
i189
 
0.1%
M114
 
0.1%
n114
 
0.1%
t114
 
0.1%
r114
 
0.1%
e114
 
0.1%
d114
 
0.1%
Other values (3)225
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)156759
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o52039
33.2%
L51811
33.1%
w51811
33.1%
i189
 
0.1%
M114
 
0.1%
n114
 
0.1%
t114
 
0.1%
r114
 
0.1%
e114
 
0.1%
d114
 
0.1%
Other values (3)225
 
0.1%

country_risk_receiver
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Low
50581 
Monitored
 
904
High
 
515

Length

Max length9
Median length3
Mean length3.1142115
Min length3

Characters and Unicode

Total characters161939
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowLow
3rd rowLow
4th rowLow
5th rowLow

Common Values

ValueCountFrequency (%)
Low50581
97.3%
Monitored904
 
1.7%
High515
 
1.0%

Length

2026-02-07T11:28:22.686630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-07T11:28:22.719416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
low50581
97.3%
monitored904
 
1.7%
high515
 
1.0%

Most occurring characters

ValueCountFrequency (%)
o52389
32.4%
L50581
31.2%
w50581
31.2%
i1419
 
0.9%
M904
 
0.6%
n904
 
0.6%
t904
 
0.6%
r904
 
0.6%
e904
 
0.6%
d904
 
0.6%
Other values (3)1545
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)161939
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o52389
32.4%
L50581
31.2%
w50581
31.2%
i1419
 
0.9%
M904
 
0.6%
n904
 
0.6%
t904
 
0.6%
r904
 
0.6%
e904
 
0.6%
d904
 
0.6%
Other values (3)1545
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)161939
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o52389
32.4%
L50581
31.2%
w50581
31.2%
i1419
 
0.9%
M904
 
0.6%
n904
 
0.6%
t904
 
0.6%
r904
 
0.6%
e904
 
0.6%
d904
 
0.6%
Other values (3)1545
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)161939
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o52389
32.4%
L50581
31.2%
w50581
31.2%
i1419
 
0.9%
M904
 
0.6%
n904
 
0.6%
t904
 
0.6%
r904
 
0.6%
e904
 
0.6%
d904
 
0.6%
Other values (3)1545
 
1.0%

cross_border
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
False
41887 
True
10113 
ValueCountFrequency (%)
False41887
80.6%
True10113
 
19.4%
2026-02-07T11:28:22.746688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

sanctions_screening_hit
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
False
51998 
True
 
2
ValueCountFrequency (%)
False51998
> 99.9%
True2
 
< 0.1%
2026-02-07T11:28:22.770662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2026-02-07T11:28:16.577301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:14.889527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:15.199997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:15.530125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:15.830535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:16.152737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:16.630692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:14.945739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:15.264605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:15.580002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:15.882868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:16.208390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:16.679641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:14.998393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:15.320801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:15.633013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:15.943454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:16.368547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:16.726295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:15.047788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:15.379500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:15.680269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:15.997075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:16.416432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:16.775393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:15.098767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:15.431072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:15.733668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:16.048875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:16.471060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:16.823298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:15.150167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:15.482898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:15.782269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:16.101385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-07T11:28:16.521269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-07T11:28:22.827648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
amount_brlamount_origauth_3dscapture_methodcard_brandcard_presentchannelcountry_risk_geocountry_risk_ipcountry_risk_receivercountry_risk_sendercross_bordercurrencydevice_rootedecifx_to_brlgeo_countrygeolocation_latgeolocation_loninstallmentsip_anomalyip_countryip_proxy_vpn_torissuing_or_acquiringmccpayment_methodpixpix_flowreceiver_countryreceiver_entity_typesanctions_screening_hitsender_countrysender_entity_typestatustransaction_type
amount_brl1.0000.9570.0120.0070.0040.0000.0000.0000.0000.0000.0000.0000.0120.0000.0270.0120.000-0.004-0.0000.0010.0000.0000.0000.0000.0000.0160.0050.0000.0000.0000.0000.0000.0000.0000.005
amount_orig0.9571.0000.0040.0050.0040.0000.0000.0000.0000.0000.0000.0110.0320.0000.0220.0320.000-0.076-0.038-0.0220.0000.0000.0250.0000.0040.0000.0140.0000.0000.0000.0000.0000.0060.0000.012
auth_3ds0.0120.0041.0000.7350.0001.0000.0000.0000.0000.0000.0000.0000.0000.0060.5160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0000.0001.000
capture_method0.0070.0050.7351.0000.0031.0000.3550.0000.0020.0000.0110.0000.1840.0060.7140.1840.0020.0000.0000.1390.0000.0070.0280.0170.0000.0101.0000.0090.0050.0110.0370.0050.0060.0001.000
card_brand0.0040.0040.0000.0031.0000.0000.0160.0050.0090.0000.0110.0040.0000.0000.0120.0000.0000.0040.0000.0080.0000.0180.0000.0000.0010.0001.0000.0000.0000.0001.0000.0260.0000.0001.000
card_present0.0000.0001.0001.0000.0001.0000.0000.0000.0000.0000.0020.0000.0160.0001.0000.0160.0180.0000.0000.0070.0000.0150.0000.0000.0000.0021.0000.0000.0130.0001.0000.0130.0000.0141.000
channel0.0000.0000.0000.3550.0160.0001.0000.0000.0000.0000.0000.0000.1110.0020.0000.1110.0070.0000.0080.1090.0000.0000.0000.0000.0000.0000.6160.0000.0000.0000.0050.0110.0000.0050.435
country_risk_geo0.0000.0000.0000.0000.0050.0000.0001.0000.1930.9370.3430.3630.1000.0000.0000.1001.0000.4010.5400.0040.0040.2060.0000.0570.0620.0050.0040.0110.9370.0530.0580.3430.0090.0040.004
country_risk_ip0.0000.0000.0000.0020.0090.0000.0000.1931.0000.0000.5650.0730.0150.0070.0000.0150.1980.0670.0970.0000.8411.0000.0000.0440.0080.0000.0000.1040.0000.0500.0000.5650.1000.0060.000
country_risk_receiver0.0000.0000.0000.0000.0000.0000.0000.9370.0001.0000.0040.3410.0970.0000.0000.0970.9380.3840.5110.0000.0070.0070.0000.0970.0670.0000.0070.0551.0000.0960.0620.0000.0560.0000.006
country_risk_sender0.0000.0000.0000.0110.0110.0020.0000.3430.5650.0041.0000.1230.0240.0000.0000.0240.3520.1190.1750.0000.0000.6020.0310.1030.0090.0000.0000.1850.0040.1050.0001.0000.1800.0120.000
cross_border0.0000.0110.0000.0000.0040.0000.0000.3630.0730.3410.1231.0000.4180.0000.0000.4181.0000.9820.9140.0000.0040.2580.0000.1500.0760.0060.0000.0240.9270.1490.0080.3090.0250.0000.000
currency0.0120.0320.0000.1840.0000.0160.1110.1000.0150.0970.0240.4181.0000.0000.0001.0000.2990.2920.2750.0520.0000.0760.0310.1020.0240.0040.2551.0000.2790.0640.0430.0930.0050.0000.184
device_rooted0.0000.0000.0060.0060.0000.0000.0020.0000.0070.0000.0000.0000.0001.0000.0000.0000.0130.0000.0040.0000.0040.0170.0000.0090.0000.0000.0000.0000.0130.0000.0000.0150.0000.0000.007
eci0.0270.0220.5160.7140.0121.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0080.0180.0000.0000.0000.0720.0110.0000.0001.0000.0000.0000.0111.0000.0000.0000.0001.000
fx_to_brl0.0120.0320.0000.1840.0000.0160.1110.1000.0150.0970.0240.4181.0000.0000.0001.0000.2990.2920.2750.0520.0000.0760.0310.1020.0240.0040.2551.0000.2790.0640.0430.0930.0050.0000.184
geo_country0.0000.0000.0000.0020.0000.0180.0071.0000.1980.9380.3521.0000.2990.0130.0000.2991.0000.5970.7330.0000.0000.2410.0000.1490.1190.0210.0000.0280.9370.1490.1050.3450.0280.0000.000
geolocation_lat-0.004-0.0760.0000.0000.0040.0000.0000.4010.0670.3840.1190.9820.2920.0000.0080.2920.5971.0000.260-0.0070.0000.1460.0100.147-0.0230.0100.0000.0300.5630.1450.0210.1880.0290.0050.000
geolocation_lon-0.000-0.0380.0000.0000.0000.0000.0080.5400.0970.5110.1750.9140.2750.0040.0180.2750.7330.2601.000-0.0060.0060.1910.0000.137-0.0210.0130.0000.0250.6870.1340.0310.2480.0250.0000.000
installments0.001-0.0220.0000.1390.0080.0070.1090.0040.0000.0000.0000.0000.0520.0000.0000.0520.000-0.007-0.0061.0000.0000.0000.0000.0000.0020.4460.3071.0000.0000.0000.0000.0070.0000.0000.241
ip_anomaly0.0000.0000.0000.0000.0000.0000.0000.0040.8410.0070.0000.0040.0000.0040.0000.0000.0000.0000.0060.0001.0000.8450.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.000
ip_country0.0000.0000.0000.0070.0180.0150.0000.2061.0000.0070.6020.2580.0760.0170.0000.0760.2410.1460.1910.0000.8451.0000.0240.2190.0050.0000.0170.3700.0010.2170.0000.6980.3730.0000.010
ip_proxy_vpn_tor0.0000.0250.0000.0280.0000.0000.0000.0000.0000.0000.0310.0000.0310.0000.0720.0310.0000.0100.0000.0000.0000.0241.0000.0000.0200.0000.0000.0000.0000.0001.0000.0200.0000.0000.022
issuing_or_acquiring0.0000.0000.0000.0170.0000.0000.0000.0570.0440.0970.1030.1500.1020.0090.0110.1020.1490.1470.1370.0000.0000.2190.0001.0000.0370.0131.0000.0000.2601.0001.0000.2610.5820.0071.000
mcc0.0000.0040.0000.0000.0010.0000.0000.0620.0080.0670.0090.0760.0240.0000.0000.0240.119-0.023-0.0210.0020.0000.0050.0200.0371.0000.0000.0000.0120.1260.0260.0000.0120.0190.0000.000
payment_method0.0160.0000.0000.0100.0000.0020.0000.0050.0000.0000.0000.0060.0040.0000.0000.0040.0210.0100.0130.4460.0000.0000.0000.0130.0001.0001.0000.0000.0080.0131.0000.0050.0120.0071.000
pix0.0050.0141.0001.0001.0001.0000.6160.0040.0000.0070.0000.0000.2550.0001.0000.2550.0000.0000.0000.3070.0000.0170.0001.0000.0001.0001.0001.0000.0000.0000.0010.0060.0000.0001.000
pix_flow0.0000.0000.0000.0090.0000.0000.0000.0110.1040.0550.1850.0241.0000.0000.0001.0000.0280.0300.0251.0000.0000.3700.0000.0000.0120.0001.0001.0000.1500.5851.0000.4531.0000.0131.000
receiver_country0.0000.0000.0000.0050.0000.0130.0000.9370.0001.0000.0040.9270.2790.0130.0000.2790.9370.5630.6870.0000.0000.0010.0000.2600.1260.0080.0000.1501.0000.2600.1120.0000.1510.0000.000
receiver_entity_type0.0000.0000.0000.0110.0000.0000.0000.0530.0500.0960.1050.1490.0640.0000.0110.0640.1490.1450.1340.0000.0090.2170.0001.0000.0260.0130.0000.5850.2601.0000.0000.2650.5850.0000.000
sanctions_screening_hit0.0000.0001.0000.0371.0001.0000.0050.0580.0000.0620.0000.0080.0430.0001.0000.0430.1050.0210.0310.0000.0000.0001.0001.0000.0001.0000.0011.0000.1120.0001.0000.0000.0000.0000.027
sender_country0.0000.0000.0000.0050.0260.0130.0110.3430.5650.0001.0000.3090.0930.0150.0000.0930.3450.1880.2480.0070.0000.6980.0200.2610.0120.0050.0060.4530.0000.2650.0001.0000.4540.0000.000
sender_entity_type0.0000.0060.0000.0060.0000.0000.0000.0090.1000.0560.1800.0250.0050.0000.0000.0050.0280.0290.0250.0000.0000.3730.0000.5820.0190.0120.0001.0000.1510.5850.0000.4541.0000.0070.000
status0.0000.0000.0000.0000.0000.0140.0050.0040.0060.0000.0120.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0070.0000.0070.0000.0130.0000.0000.0000.0000.0071.0000.000
transaction_type0.0050.0121.0001.0001.0001.0000.4350.0040.0000.0060.0000.0000.1840.0071.0000.1840.0000.0000.0000.2410.0000.0100.0221.0000.0001.0001.0001.0000.0000.0000.0270.0000.0000.0001.000

Missing values

2026-02-07T11:28:16.954277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-07T11:28:17.199109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-02-07T11:28:17.486135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

transaction_idtimestamptransaction_typesender_idsender_entity_typereceiver_idreceiver_entity_typeamount_brlamount_origcurrencyfx_to_brlstatuschannelcapture_methodpayment_methodinstallmentsissuing_or_acquiringpixpix_flowcard_brandcard_presentauth_3dsecimccgeo_countrygeolocation_latgeolocation_lonip_countryip_anomalyip_proxy_vpn_tordevice_fingerprintip_addressdevice_rootedsender_countryreceiver_countrycountry_risk_geocountry_risk_ipcountry_risk_sendercountry_risk_receivercross_bordersanctions_screening_hit
0T9HIMVHJ8TMK72025-09-29T01:19:37PIXC100602customerM200223merchant3478.733478.73BRL1ConfirmedAppCopyPasteNaN1NaNYescash_outNaNNaNNaNNaN6051BR-11.743364-54.286939DENoNaNpoq50ptmz8g3pgoh193.59.218.134NoBRBRLowLowLowLowNoNo
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2TYVX10N3OXT1H2025-07-01T22:36:33PIXC101811customerC101070customer1595.141595.14BRL1ConfirmedAppCopyPasteNaN1NaNYescash_outNaNNaNNaNNaN4111BR-17.208767-66.365148BRNoNaNbnv7ad7zrmn7cgfh169.185.50.15NoBRBRLowLowLowLowNoNo
3TEXB9LV0C1BOI2025-07-28T09:01:56PIXC100023customerC101820customer1074.711074.71BRL1ConfirmedAppPix KeyNaN1NaNYescash_outNaNNaNNaNNaN5945BR-8.096121-57.440608BRNoVPNh96qnwm3yju8wdd9150.97.162.214NoBRBRLowLowLowLowNoNo
4T9HZ6Z1DWO12V2025-07-27T00:36:40PIXM200612merchantC101267customer6821.966821.96BRL1PendingAPIQR StaticNaN1NaNYescash_inNaNNaNNaNNaN6211BR-16.441953-54.044138BRNoNaNelmh8kqwykznkcfz23.56.92.120NoBRBRLowLowLowLowNoNo
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8TX0GK853YYAWO2025-07-18T16:27:49CardC100202customerM200315merchant5183.665183.66BRL1ConfirmedAppNFCcredit4acquiringNoNaNMastercardYesNaNNaN4814BR-19.960702-70.383821BRNoNaNb3od13zwwn6ar81d155.240.142.23NoBRBRLowLowLowLowNoNo
9TUILBKR4582YE2025-07-24T07:43:05PIXC100384customerM200731merchant32104.5132104.51BRL1ConfirmedAppPix KeyNaN1NaNYescash_outNaNNaNNaNNaN5411BR-32.593663-39.621846BRNoNaN1qyg9doxytdb9quq140.177.222.163NoBRBRLowLowLowLowNoNo
transaction_idtimestamptransaction_typesender_idsender_entity_typereceiver_idreceiver_entity_typeamount_brlamount_origcurrencyfx_to_brlstatuschannelcapture_methodpayment_methodinstallmentsissuing_or_acquiringpixpix_flowcard_brandcard_presentauth_3dsecimccgeo_countrygeolocation_latgeolocation_lonip_countryip_anomalyip_proxy_vpn_tordevice_fingerprintip_addressdevice_rootedsender_countryreceiver_countrycountry_risk_geocountry_risk_ipcountry_risk_sendercountry_risk_receivercross_bordersanctions_screening_hit
51990TRK52H5I9XMJD2025-08-04T03:49:34PIXC101375customerM200889merchant4692.074692.07BRL1FailedAppPix KeyNaN1NaNYescash_outNaNNaNNaNNaN5411BR-20.956299-56.080469BRNoNaNzdkl7ejo5iexcbxk149.210.33.41NoBRBRLowLowLowLowNoNo
51991T9E8KJ4KMI0T02025-08-19T07:43:31PIXC102000customerM200945merchant5901.615901.61BRL1ConfirmedAPIQR StaticNaN1NaNYescash_outNaNNaNNaNNaN4111DE47.18337211.794815BRNoNaN9umgcatryormxwrd138.254.107.249NoBRDELowLowLowLowYesNo
51992TYS45VQY6P0LD2025-07-12T21:53:52PIXC101613customerM200468merchant1443.881443.88BRL1ConfirmedAppPix KeyNaN1NaNYescash_outNaNNaNNaNNaN6011BR2.133016-52.778961BRNoNaNyb8atb183juoi82d118.206.185.28NoBRBRLowLowLowLowNoNo
51993T55L3Z7BOYW5N2025-08-05T09:42:11CardC101983customerM200245merchant702.85702.85BRL1PendingTerminalNFCcredit1acquiringNoNaNMastercardYesNaNNaN5411BR-28.364145-50.054924BRNoNaN8taqjnkjvao4fum4233.56.178.153NoBRBRLowLowLowLowNoNo
51994TNOS23AT6UXP62025-08-19T17:02:58PIXC100165customerM200191merchant33523.7333523.73BRL1ConfirmedAPIPix KeyNaN1NaNYescash_outNaNNaNNaNNaN6011BR1.882078-64.675605BRNoNaNmc11zbypmg951ue619.143.41.197NoBRBRLowLowLowLowNoNo
51995T49V4YOKJLPQW2025-07-29T19:49:19PIXC101510customerM200416merchant5199.825199.82BRL1ConfirmedAppCopyPasteNaN1NaNYescash_outNaNNaNNaNNaN6211BR-28.892878-65.820391BRNoNaN7rtszixrvg221fz2202.37.132.125NoBRBRLowLowLowLowNoNo
51996TWK0BCFWD8M102025-07-06T13:45:49PIXC102205customerM200847merchant3488.233488.23BRL1ConfirmedAPIQR DynamicNaN1NaNYescash_outNaNNaNNaNNaN4789ES40.0642821.736396BRNoNaNzndnidj9dakimdda35.157.144.28NoBRESLowLowLowLowYesNo
51997T09E7BGTZEOHK2025-09-20T09:02:01CardM200016merchantC102401customer2314.512314.51BRL1ConfirmedWebMOTOdebit1issuingNoNaNMastercardNoNo7.05411BR-5.934246-72.775044BRNoNaNptzo8rfwvy9c88dn58.157.65.71NoBRBRLowLowLowLowNoNo
51998TKS3QBCQ5F7Y42025-09-15T00:20:18PIXM200227merchantC100948customer1655.201655.20BRL1PendingAPICopyPasteNaN1NaNYescash_inNaNNaNNaNNaN6011FR43.2666351.580092FRNoNaNz8zdg6q07644pry775.153.164.128YesFRBRLowLowLowLowYesNo
51999T2VCTGH0S2KVJ2025-07-27T19:49:48PIXM200178merchantC101632customer2784.422784.42BRL1ConfirmedAppQR StaticNaN1NaNYescash_inNaNNaNNaNNaN6051BR-12.792722-57.939066BRNoNaNpwyubjnaddoo58kx84.57.205.20NoBRBRLowLowLowLowNoNo